Latent Class Analysis of Multiple Health Risk Behaviors among Australian University Students and Associations with Psychological Distress.

University students have high rates of health risk behaviors and psychological distress. This study explores patterns of health behaviors among a sample of Australian university students, and determines whether patterns of health behaviors are associated with psychological distress and demographic characteristics. Cross-sectional data from the University of Newcastle Student Healthy Lifestyle Survey 2019 were analyzed. Fruit and vegetable intake, sugar-sweetened beverage intake, physical activity, sitting time, smoking, alcohol intake, drug use, sleep and psychological distress were assessed. Latent class analysis (LCA) was used to identify patterns of health risk behaviors, and latent class regression to explore associations between psychological distress and demographic characteristics with health behavior classes. Analysis included 1965 students (mean age 25.8 ± 8.6 years, 70.7% female). Three patterns of health behaviors were identified: healthier (48.6%), moderate (40.2%) and unhealthy (11.2%) lifestyle classes. Students in the moderate and unhealthy lifestyle classes had higher odds of moderate (OR 1.43 and 2.37) and high/very high psychological distress risk (OR 2.71 and 11.69). Students in the unhealthy and moderate lifestyle classes had a higher odds of being male, younger, enrolled in transition to university and English language courses, Aboriginal or Torres Strait Islander descent and to report some financial difficulty. Study findings may be used to inform the design of mental health interventions for university students that target key health risk behaviors.

Associations between multiple positive health behaviors and cardiometabolic risk using 3 alternative measures of physical activity: NHANES 2005-2006.

The study aimed to investigate the association between clustered cardiometabolic risk (CCMR) and health-behavior indices comprising 3 different measures of physical activity, screen time, diet and sleep in NHANES 2005-2006. CCMR was calculated by standardizing and summarizing measures of blood pressure, fasting glucose, triglycerides, insulin, high-density lipoprotein and waist circumference to create a z score. Three health behavior indices were constructed with a single point allocated to each of the following lower risk behaviors: muscle strengthening activity, healthy eating score, sleep disorder/disruption, sleep duration, screen time and physical activity (self-reported moderate-to-vigorous physical activity [MVPA] (Index Score-SR), accelerometer-measured MVPA (Index Score-MVPA) or accelerometer-measured steps Index Score-Steps). Linear regression models explored associations between index scores and CCMR. In the sample (n = 1537, 52% male, aged 45.5 [SE: 0.9] years), reporting 0-5 vs. 6 health behaviors using Index Score-SR and Index Score-MVPA, and 0-4 vs. 6 health behaviors using Index Score-Steps, were associated with a significantly higher CCMR. The beta (ß [95% CI]) for zero vs. 6 behaviors were Index Score-SR (2.86 [2.02, 3.69], Index Score-MVPA (2.41 [1.49, 3.33] and Index Score-Steps (2.41 [1.68, 3.15]). Irrespective of the measure of physical activity, engaging in fewer positive health behaviors was associated with greater CCMR. Novelty: Physical activity, screen time, diet and sleep may exert synergistic/cumulative effects on clustered cardiometabolic risk. A greater number of positive health behaviors was associated with a lower clustered cardiometabolic risk factor score. The reduction in cardiometabolic risk was similar irrespective of which physical activity measure was used.

Daily steps and diet, but not sleep, are related to mortality in older Australians.

OBJECTIVES: Supporting healthy ageing is a key priority worldwide. Physical activity, diet quality and sleep are all associated with health outcomes, but few studies have explored their independent associations with all-cause mortality in an older population in the same model. The study aim was to examine associations between step-count, self-reported diet quality, restless sleep, and all-cause mortality in adults aged 55-85 years. DESIGN: A prospective cohort study of adults in Newcastle, New South Wales, Australia. METHOD: Data were from 1697 participants (49.3% women; baseline mean age 65.4 ±â€¯7.1 years). Daily steps (measured by pedometer), diet quality (from a modified Australian Recommended Food Score), and frequency of restless sleep (by self-report) were assessed in relation to all-cause mortality using Cox proportional hazard regression with adjustment for sex, age, household income and smoking. Baseline data were collected between January 2005 and April 2008, and last follow-up was in March 2017 (median follow-up 9.6 years). RESULTS: Higher step count (HR: 0.93, 95%CI: 0.88-0.98 per 1000-step increment) and higher diet quality (HR: 0.86, 95%CI: 0.74-0.99 per 8-point increment in diet quality score) were associated with reduced mortality risk. Restless sleep for ≥3 nights/week was not associated with mortality risk (HR: 1.03, 95%CI: 0.78-1.39). Sensitivity analyses, adjusting for chronic disease and excluding deaths <1 year after baseline, did not change these estimates. CONCLUSIONS: Increased daily steps and consumption of a greater variety of nutrient-dense foods every week would result in substantial health benefits for older people. Future research should include a greater variety of sleep measures.

Feasibility and Preliminary Efficacy of an m-Health Intervention Targeting Physical Activity, Diet, and Sleep Quality in Shift-Workers.

Poor health behaviors are prevalent in shift-workers, but few multiple health-behavior interventions consider their unique needs. This study aimed to (1) evaluate the feasibility and acceptability of an existing app-based intervention to improve physical activity, diet, and sleep quality in a shift-worker population, (2) estimate intervention effect in a four-week pilot randomized controlled trial (RCT) (ACTRN12618001785291). Shift-workers (18-65 years old) were randomized to intervention (n = 20) or wait-list (n = 20) groups. Outcomes included recruitment, engagement, attrition, usefulness ratings, System Usability Scale (SUS), qualitative interviews, and estimation of treatment effect (minutes of physical activity, diet quality, and sleep quality) using mixed model analysis. Recruitment took one week. App-use at week four was 55% (11/20), 85% (34/40) completed the four-week follow-up questionnaire, and 20% (4/20) of the intervention group completed the qualitative interview. The intervention was rated as slightly to moderately useful by 76.9% (10/13) of participants on a five-point scale. The SUS score was 62.7 (12.7) out of 100. Diet quality improved for the intervention (4.5 points; 95% confidence interval (CI) = 0.1, 8.9; p = 0.047) vs. the wait-list group, but not physical activity or sleep quality. Qualitative interviews found that a more tailored intervention, more accessible information, and interactive features were desired. The intervention was feasible in terms of recruitment, but modifications to increase engagement are needed.

Patterns of Diet, Physical Activity, Sitting and Sleep Are Associated with Socio-Demographic, Behavioural, and Health-Risk Indicators in Adults.

Our understanding of how multiple health-behaviours co-occur is in its infancy. This study aimed to: (1) identify patterns of physical activity, diet, sitting, and sleep; and (2) examine the association between sociodemographic and health-risk indicators. Pooled data from annual cross-sectional telephone surveys of Australian adults (2015-2017, n = 3374, 51.4% women) were used. Participants self-reported physical activity, diet, sitting-time, sleep/rest insufficiency, sociodemographic characteristics, smoking, alcohol use, height and weight to calculate body mass index (BMI), and mental distress frequency. Latent class analysis identified health-behaviour classes. Latent class regression determined the associations between health-behaviour patterns, sociodemographic, and health-risk indicators. Three latent classes were identified. Relative to a 'moderate lifestyle' pattern (men: 43.2%, women: 38.1%), a 'poor lifestyle' pattern (men: 19.9%, women: 30.5%) was associated with increased odds of a younger age, smoking, BMI ≥ 30.0 kg/m2, frequent mental distress (men and women), non-partnered status (men only), a lower Socioeconomic Index for Areas centile, primary/secondary education only, and BMI = 25.0-29.9 kg/m2 (women only). An 'active poor sleeper' pattern (men: 37.0%, women: 31.4%) was associated with increased odds of a younger age (men and women), working and frequent mental distress (women only), relative to a 'moderate lifestyle' pattern. Better understanding of how health-behaviour patterns influence future health status is needed. Targeted interventions jointly addressing these behaviours are a public health priority.

Associations of health-behavior patterns, mental health and self-rated health.

Diet quality, physical activity, alcohol use, smoking, sleep and sitting-time are behaviors known to influence health. The aims of this study were to identify how these behaviors co-occur to form distinct health-behavior patterns, and to investigate the relationship between these patterns, and mental and self-rated health. Members of the Australian 10,000 Steps project were invited to participate in an online survey in November-December 2011. The participants self-reported demographic and behavioral characteristics (fruit and vegetable intake, fast food, soft drink and alcohol consumption, smoking, physical activity, sitting-time and sleep), frequency of mental distress and self-rated health. Latent Class Analysis was used to identify health-behavior patterns. Latent class regression was used to examine relationships between behavior patterns, mental and self-rated health, and socio-demographic and economic factors. Data were analyzed in October 2017. Complete datasets were obtained from 10,638 participants. Four latent classes were identified, characterized by 'Low-Risk Behavior', 'Poor Sleep, Low-Risk Daytime Behavior', 'Sound Sleep, High-Risk Daytime Behavior' and 'High-Risk Behavior'. The latter two classes, both characterized by high-risk daytime behaviors, were associated with poor self-rated health. Participants in classes with high-risk daytime behaviors were more likely to be younger, non-partnered, non-university educated, from lower income households and work longer hours. Classes characterized by poor sleep quality were associated with higher frequency of mental distress. Findings suggest that experiencing poor sleep is partly independent of daytime behaviors, demographic and socioeconomic factors, but has a strong association with mental health.

Body composition, diet, and physical activity: a longitudinal cohort study in preschoolers with cerebral palsy.

BACKGROUND: Altered body composition in children with cerebral palsy (CP) could be due to differences in energy intake, habitual physical activity (HPA), and sedentary time. OBJECTIVE: We investigated the longitudinal relation between the weight-for-age z score (WZ), fat-free mass (FFM), percentage of body fat (%BF), and modifiable lifestyle factors for all Gross Motor Function Classification System (GMFCS) levels (I-V). DESIGN: The study was a longitudinal population-based cohort study of children with CP who were aged 18-60 mo (364 assessments in 161 children; boys: 61%; mean ± SD recruitment age: 2.8 ± 0.9 y; GMFCS: I, 48%; II, 11%; III, 15%; IV, 11%; and V, 15%). A deuterium dilution technique or bioelectrical impedance analysis was used to estimate FFM, and the %BF was calculated. Energy intake, HPA, and sedentary time were measured with the use of a 3-d weighed food diary and accelerometer wear. Data were analyzed with the use of a mixed-model analysis. RESULTS: Children in GMFCS group I did not differ from age- and sex-specific reference children with typical development for weight. Children in GMFCS group IV were lighter-for-age, and children in GMFCS group V had a lower FFM-for-height than those in GMFCS group I. Children in GMFCS groups II-V had a higher %BF than that of children in GMFCS group I, with the exception of orally fed children in GMFCS group V. The mean %BF of children with CP classified them as overfat or obese. There was a positive association between energy intake and FFM and also between HPA level and FFM for children in GMFCS group I. CONCLUSIONS: Altered body composition was evident in preschool-age children with CP across functional capacities. Gross motor function, feeding method, energy intake, and HPA level in GMFCS I individuals are the strongest predictors of body composition in children with CP between the ages of 18 and 60 mo.

Validation of Accelerometer Cut-Points in Children With Cerebral Palsy Aged 4 to 5 Years.

PURPOSE: To derive and validate triaxial accelerometer cut-points in children with cerebral palsy (CP) and compare these with previously established cut-points in children with typical development. METHODS: Eighty-four children with CP aged 4 to 5 years wore the ActiGraph during a play-based gross motor function measure assessment that was video-taped for direct observation. Receiver operating characteristic and Bland-Altman plots were used for analyses. RESULTS: The ActiGraph had good classification accuracy in Gross Motor Function Classification System (GMFCS) levels III and V and fair classification accuracy in GMFCS levels I, II, and IV. These results support the use of the previously established cut-points for sedentary time of 820 counts per minute in children with CP aged 4 to 5 years across all functional abilities. CONCLUSIONS: The cut-point provides an objective measure of sedentary and active time in children with CP. The cut-point is applicable to group data but not for individual children.

Longitudinal Growth, Diet, and Physical Activity in Young Children With Cerebral Palsy.

OBJECTIVES: To describe the longitudinal relationship between height-for-age z score (HZ), growth velocity z score, energy intake, habitual physical activity (HPA), and sedentary time across Gross Motor Function Classification System (GMFCS) levels I to V in preschoolers with cerebral palsy (CP). METHODS: Children with CP (n = 175 [109 (62.2%) boys]; mean recruitment age 2 years, 10 months [SD 11 months]; GMFCS I = 83 [47.2%], II = 21 [11.9%], III = 28 [15.9%], IV = 19 [10.8%], V = 25 [14.2%]) were assessed 440 times between the age of 18 months and 5 years. Height/length ratio was measured or estimated via knee height. Population-based standards were used to calculate HZ and growth velocity z-score by age and sex categories. Feeding method (oral or tube) and gestational age at birth (GA) were collected from parents. Three-day ActiGraph and food diary data were used to measure HPA/sedentary time ratio and energy intake, respectively. Oropharyngeal dysphagia was rated with the Dysphagia Disorder Survey (part 2, Pediatric). Analysis was undertaken with mixed-effects regression models. RESULTS: For GMFCS level I, height and growth velocity did not differ from population-level growth standards. Children in levels II to V were significantly shorter, and those in levels III to V grew significantly more slowly than those in level I. There was a significant positive association between HZ and GA at all GMFCS levels. Energy intake, HPA, sedentary time, Dysphagia Disorder Survey score, and feeding method were not significantly associated with either height or growth velocity once GMFCS level was accounted for. CONCLUSIONS: Functional status and GA should be considered when assessing the growth of a child with CP. Research into interventions aimed at increasing active movement in GMFCS levels III to V and their efficacy in improving growth and health outcomes is warranted.